{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "#importing all the required libraries\n", "import pandas as pd\n", "import numpy as np\n", "import sklearn as sk\n", "import pickle\n", "from sklearn.feature_extraction.text import CountVectorizer" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv('deceptive-opinion.csv')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

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	deceptive	hotel	polarity	source	text
0	truthful	conrad	positive	TripAdvisor	We stayed for a one night getaway with family ...
1	truthful	hyatt	positive	TripAdvisor	Triple A rate with upgrade to view room was le...
2	truthful	hyatt	positive	TripAdvisor	This comes a little late as I'm finally catchi...
3	truthful	omni	positive	TripAdvisor	The Omni Chicago really delivers on all fronts...
4	truthful	hyatt	positive	TripAdvisor	I asked for a high floor away from the elevato...

\n", "

" ], "text/plain": [ " deceptive hotel polarity source \\\n", "0 truthful conrad positive TripAdvisor \n", "1 truthful hyatt positive TripAdvisor \n", "2 truthful hyatt positive TripAdvisor \n", "3 truthful omni positive TripAdvisor \n", "4 truthful hyatt positive TripAdvisor \n", "\n", " text \n", "0 We stayed for a one night getaway with family ... \n", "1 Triple A rate with upgrade to view room was le... \n", "2 This comes a little late as I'm finally catchi... \n", "3 The Omni Chicago really delivers on all fronts... \n", "4 I asked for a high floor away from the elevato... " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

	deceptive	hotel	polarity	source	text
1595	deceptive	intercontinental	negative	MTurk	Problems started when I booked the InterContin...
1596	deceptive	amalfi	negative	MTurk	The Amalfi Hotel has a beautiful website and i...
1597	deceptive	intercontinental	negative	MTurk	The Intercontinental Chicago Magnificent Mile ...
1598	deceptive	palmer	negative	MTurk	The Palmer House Hilton, while it looks good i...
1599	deceptive	amalfi	negative	MTurk	As a former Chicagoan, I'm appalled at the Ama...

\n", "

" ], "text/plain": [ " deceptive hotel polarity source \\\n", "1595 deceptive intercontinental negative MTurk \n", "1596 deceptive amalfi negative MTurk \n", "1597 deceptive intercontinental negative MTurk \n", "1598 deceptive palmer negative MTurk \n", "1599 deceptive amalfi negative MTurk \n", "\n", " text \n", "1595 Problems started when I booked the InterContin... \n", "1596 The Amalfi Hotel has a beautiful website and i... \n", "1597 The Intercontinental Chicago Magnificent Mile ... \n", "1598 The Palmer House Hilton, while it looks good i... \n", "1599 As a former Chicagoan, I'm appalled at the Ama... " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.tail()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

	deceptive	text
0	truthful	We stayed for a one night getaway with family ...
1	truthful	Triple A rate with upgrade to view room was le...
2	truthful	This comes a little late as I'm finally catchi...
3	truthful	The Omni Chicago really delivers on all fronts...
4	truthful	I asked for a high floor away from the elevato...
...	...	...
1595	deceptive	Problems started when I booked the InterContin...
1596	deceptive	The Amalfi Hotel has a beautiful website and i...
1597	deceptive	The Intercontinental Chicago Magnificent Mile ...
1598	deceptive	The Palmer House Hilton, while it looks good i...
1599	deceptive	As a former Chicagoan, I'm appalled at the Ama...

\n", "

1600 rows × 2 columns

\n", "

" ], "text/plain": [ " deceptive text\n", "0 truthful We stayed for a one night getaway with family ...\n", "1 truthful Triple A rate with upgrade to view room was le...\n", "2 truthful This comes a little late as I'm finally catchi...\n", "3 truthful The Omni Chicago really delivers on all fronts...\n", "4 truthful I asked for a high floor away from the elevato...\n", "... ... ...\n", "1595 deceptive Problems started when I booked the InterContin...\n", "1596 deceptive The Amalfi Hotel has a beautiful website and i...\n", "1597 deceptive The Intercontinental Chicago Magnificent Mile ...\n", "1598 deceptive The Palmer House Hilton, while it looks good i...\n", "1599 deceptive As a former Chicagoan, I'm appalled at the Ama...\n", "\n", "[1600 rows x 2 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Extracting only the requireed features\n", "df1 = df[['deceptive', 'text']]\n", "df1" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\dell\\AppData\\Local\\Temp\\ipykernel_13052\\3325549381.py:2: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " df1.loc[df1['deceptive'] == 'deceptive', 'deceptive'] = 0\n", "C:\\Users\\dell\\AppData\\Local\\Temp\\ipykernel_13052\\3325549381.py:3: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " df1.loc[df1['deceptive'] == 'truthful', 'deceptive'] = 1\n" ] } ], "source": [ "#filling the categorical variable deceptive with 0 for fake review and 1 for real review\n", "df1.loc[df1['deceptive'] == 'deceptive', 'deceptive'] = 0\n", "df1.loc[df1['deceptive'] == 'truthful', 'deceptive'] = 1" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

	deceptive	text
0	1	We stayed for a one night getaway with family ...
1	1	Triple A rate with upgrade to view room was le...
2	1	This comes a little late as I'm finally catchi...
3	1	The Omni Chicago really delivers on all fronts...
4	1	I asked for a high floor away from the elevato...
...	...	...
1595	0	Problems started when I booked the InterContin...
1596	0	The Amalfi Hotel has a beautiful website and i...
1597	0	The Intercontinental Chicago Magnificent Mile ...
1598	0	The Palmer House Hilton, while it looks good i...
1599	0	As a former Chicagoan, I'm appalled at the Ama...

\n", "

1600 rows × 2 columns

\n", "

" ], "text/plain": [ " deceptive text\n", "0 1 We stayed for a one night getaway with family ...\n", "1 1 Triple A rate with upgrade to view room was le...\n", "2 1 This comes a little late as I'm finally catchi...\n", "3 1 The Omni Chicago really delivers on all fronts...\n", "4 1 I asked for a high floor away from the elevato...\n", "... ... ...\n", "1595 0 Problems started when I booked the InterContin...\n", "1596 0 The Amalfi Hotel has a beautiful website and i...\n", "1597 0 The Intercontinental Chicago Magnificent Mile ...\n", "1598 0 The Palmer House Hilton, while it looks good i...\n", "1599 0 As a former Chicagoan, I'm appalled at the Ama...\n", "\n", "[1600 rows x 2 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Printing Dataframe1\n", "df1" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "\n", "#Taking the input and output features seperately\n", "X = df1['text']\n", "Y = np.asarray(df1['deceptive'], dtype = int)\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "#importing MultinomialNB\n", "from sklearn.naive_bayes import MultinomialNB, GaussianNB" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "#splitting the data into training and testing set with test size is 30%\n", "from sklearn.model_selection import train_test_split\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3,random_state=109) # 70% training and 30% test" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1063 We stayed at the Ritz Carlton two weeks prior,...\n", "21 We went to Chicago to see an exhibit at the Ar...\n", "1480 I recently stayed in The James Hotel in Chicag...\n", "1215 Hyatt Regency Hotel: Good ole Downtown, Chicag...\n", "459 Me and my husband got married here. We loved t...\n", " ... \n", "133 Perfect location, clean and courteous staff al...\n", "1252 If you want a 5-star hotel with 1-star service...\n", "254 We had our hotel reservations at another hotel...\n", "386 We became an Ambassador member just before spe...\n", "1240 My experience as Fairmont Chicago Millennium P...\n", "Name: text, Length: 480, dtype: object" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_test" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0,\n", " 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1,\n", " 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1,\n", " 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1,\n", " 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0,\n", " 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,\n", " 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0,\n", " 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1,\n", " 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1,\n", " 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0,\n", " 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0,\n", " 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0,\n", " 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0,\n", " 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1,\n", " 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1,\n", " 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1,\n", " 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0,\n", " 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0,\n", " 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1,\n", " 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n", " 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0,\n", " 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0])" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_test" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "nb = MultinomialNB()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "#Converting the review (text feature) to numerical features\n", "cv = CountVectorizer()\n", "x = cv.fit_transform(X_train)\n", "y = cv.transform(X_test)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "# Fitting the model\n", "nb.fit(x, y_train)\n", "pickle.dump(nb,open('model.pkl','wb'))\n", "model=pickle.load(open('model.pkl','rb'))" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1,\n", " 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1,\n", " 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1,\n", " 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1,\n", " 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0,\n", " 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,\n", " 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0,\n", " 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1,\n", " 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0,\n", " 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0,\n", " 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0,\n", " 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0,\n", " 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0,\n", " 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0,\n", " 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1,\n", " 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1,\n", " 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0,\n", " 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0,\n", " 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1,\n", " 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0,\n", " 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0,\n", " 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0])" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nb.predict(y)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9714285714285714" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Training Accuracy\n", "nb.score(x, y_train)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.85625" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Testing Accuracy\n", "nb.score(y, y_test)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "# Implementing with pywedge\n", "import pywedge as pw " ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "#blm = pw.baseline_model(x, y_train)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "#blm.classification_summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Implementing with the SVM

" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "from sklearn import svm\n", "\n", "#Create a svm Classifier\n", "clf = svm.SVC(kernel='linear') # Linear Kernel" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

SVC(kernel='linear')

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

" ], "text/plain": [ "SVC(kernel='linear')" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Train the model using the training sets\n", "clf.fit(x, y_train)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1,\n", " 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0,\n", " 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1,\n", " 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1,\n", " 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0,\n", " 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,\n", " 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1,\n", " 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1,\n", " 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n", " 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0,\n", " 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0,\n", " 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0,\n", " 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0,\n", " 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1,\n", " 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1,\n", " 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1,\n", " 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0,\n", " 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0,\n", " 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1,\n", " 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1,\n", " 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0,\n", " 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0])" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Predict the response for test dataset\n", "y_pred = clf.predict(y)\n", "y_pred" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.0" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf.score(x, y_train) #Training accuracy" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.8166666666666667" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf.score(y, y_test)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.8" } }, "nbformat": 4, "nbformat_minor": 4 }